The existing tools that aim to derive data models from business process models are typically able to process the source models represented by one single notation and also serialized in one specific way. However, the standards (e.g., BPMN) enable different serialization formats and also provide serialization flexibility, which leads to various implementations of the standard in different modeling tools and results in differently serialized models in practice, which therefore significantly constraints usability of the existing model-driven tools. In this article, we present an approach to automatic derivation of conceptual database models from business process models represented by different notations, with particular focus on differently serialized process models. A deterministic rule-based approach is proposed to overcome the serialization specificities and to enable extraction of characteristic elements from differently serialized process models. Based on the proposed approach, we implemented an online web-based model-driven tool named AMADEOS, which is able to automatically derive conceptual database models from process models represented by different notations and also differently serialized. The experimental results show that the proposed approach and implemented tool enable successful extraction of specific elements from differently serialized process models and enable derivation of the target conceptual database models with very high completeness and precision.

旨在从业务流程模型中获取数据模型的现有工具通常能够处理由一种符号表示并且也以一种特定方式序列化的源模型。但是，这些标准（例如BPMN）支持不同的序列化格式，并且还提供序列化灵活性，这导致该标准在不同的建模工具中进行了各种实现，并在实践中导致了不同的序列化模型，因此极大地限制了现有模型的可用性-驱动工具。在本文中，我们提出了一种从以不同符号表示的业务流程模型中自动派生概念数据库模型的方法，尤其关注于不同序列化的流程模型。提出了一种基于确定性规则的方法，以克服序列化的特殊性，并能够从不同序列化的过程模型中提取特征元素。基于提出的方法，我们实现了一个在线的基于网络的模型驱动工具AMADEOS，该工具能够从以不同符号表示且序列化方式不同的过程模型中自动导出概念数据库模型。实验结果表明，所提出的方法和实现的工具能够从不同序列化的过程模型中成功提取特定元素，并能够以很高的完整性和精度来推导目标概念数据库模型。我们实现了一个基于网络的在线模型驱动工具AMADEOS，该工具能够从以不同符号表示并且序列化方式不同的流程模型中自动提取概念数据库模型。实验结果表明，所提出的方法和实现的工具能够从不同序列化的过程模型中成功提取特定元素，并能够以很高的完整性和精度来推导目标概念数据库模型。我们实现了一个基于网络的在线模型驱动工具AMADEOS，该工具能够从以不同符号表示并且序列化方式不同的流程模型中自动提取概念数据库模型。实验结果表明，所提出的方法和实现的工具能够从不同序列化的过程模型中成功提取特定元素，并能够以很高的完整性和精度来推导目标概念数据库模型。